GiniClust3: a fast and memory-efficient tool for rare cell type identification

• Published in: BMC bioinformatics Vol. 21; no. 1; pp. 158 - 7
• Main Authors: Dong, Rui, Yuan, Guo-Cheng
• Format: Journal Article
• Language: English
• Published: London BioMed Central 25.04.2020; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Algorithms; Bioinformatics; Biomedical and Life Sciences; Cell cycle; Clustering; Clusters; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Datasets; Efficiency; Gene sequencing; Genes; Genomics; Gini index; Granulocytes; Life Sciences; Mapping; Methods; Microarrays; Neurons; Perturbation methods; Population; Principal components analysis; Rare cell identification; Ribonucleic acid; RNA; RNA sequencing; Scalability; Single cell RNA-seq; Software; Source code; Transcriptome analysis; Gini index; Scalability; Rare cell identification; Single cell RNA-seq
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-020-3482-1

Background With the rapid development of single-cell RNA sequencing technology, it is possible to dissect cell-type composition at high resolution. A number of methods have been developed with the purpose to identify rare cell types. However, existing methods are still not scalable to large datasets, limiting their utility. To overcome this limitation, we present a new software package, called GiniClust3, which is an extension of GiniClust2 and significantly faster and memory-efficient than previous versions. Results Using GiniClust3, it only takes about 7 h to identify both common and rare cell clusters from a dataset that contains more than one million cells. Cell type mapping and perturbation analyses show that GiniClust3 could robustly identify cell clusters. Conclusions Taken together, these results suggest that GiniClust3 is a powerful tool to identify both common and rare cell population and can handle large dataset. GiniCluster3 is implemented in the open-source python package and available at https://github.com/rdong08/GiniClust3 .